Update README.md

README.md

@@ -12,4 +12,76 @@ pipeline_tag: automatic-speech-recognition
 license: apache-2.0
 ---
 
-# Wav2Vec2-XLS-R-1B-21-EN
+# Wav2Vec2-XLS-R-2b-21-EN
+
+Facebook's Wav2Vec2 XLS-R fine-tuned for **Speech Translation.**
+
+![model image](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/xls_r.png)
+
+This is a [SpeechEncoderDecoderModel](https://huggingface.co/transformers/model_doc/speechencoderdecoder.html) model. 
+The encoder was warm-started from the [**`facebook/wav2vec2-xls-r-1b`**](https://huggingface.co/facebook/wav2vec2-xls-r-1b) checkpoint and
+the decoder from the [**`facebook/mbart-large-50`**](https://huggingface.co/facebook/mbart-large-50) checkpoint.
+Consequently, the encoder-decoder model was fine-tuned on 21 `{lang}` -> `en` translation pairs of the [Covost2 dataset](https://huggingface.co/datasets/covost2).
+
+The model can translate from the following spoken languages `{lang}` -> `en` (English):
+
+{`fr`, `de`, `es`, `ca`, `it`, `ru`, `zh-CN`, `pt`, `fa`, `et`, `mn`, `nl`, `tr`, `ar`, `sv-SE`, `lv`, `sl`, `ta`, `ja`, `id`, `cy`} -> `en`
+
+For more information, please refer to Section *5.1.2* of the [official XLS-R paper](https://arxiv.org/abs/2111.09296).
+
+## Usage
+
+### Demo
+
+The model can be tested directly on the speech recognition widget on this model card! 
+Simple record some audio in one of the possible spoken languages or pick an example audio file to see how well the checkpoint can translate the input.
+
+### Example 
+
+As this a standard sequence to sequence transformer model, you can use the `generate` method to generate the
+transcripts by passing the speech features to the model.
+
+You can use the model directly via the ASR pipeline
+
+```python
+from datasets import load_dataset
+from transformers import pipeline
+
+# replace following lines to load an audio file of your choice
+librispeech_en = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
+audio_file = librispeech_en[0]["file"]
+
+asr = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-xls-r-1b-21-to-en", feature_extractor="facebook/wav2vec2-xls-r-1b-21-to-en")
+
+translation = asr(audio_file)
+```
+
+or step-by-step as follows:
+
+```python
+import torch
+from transformers import Speech2Text2Processor, SpeechEncoderDecoder
+from datasets import load_dataset
+
+model = SpeechEncoderDecoder.from_pretrained("facebook/wav2vec2-xls-r-1b-21-to-en")
+processor = Speech2Text2Processor.from_pretrained("facebook/wav2vec2-xls-r-1b-21-to-en")
+
+ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
+
+inputs = processor(ds[0]["audio"]["array"], sampling_rate=ds[0]["audio"]["array"]["sampling_rate"], return_tensors="pt")
+generated_ids = model.generate(input_ids=inputs["input_features"], attention_mask=inputs["attention_mask"])
+transcription = processor.batch_decode(generated_ids)
+```
+
+## Results `{lang}` -> `en`
+
+See the row of **XLS-R (1B)** for the performance on [Covost2](https://huggingface.co/datasets/covost2) for this model.
+
+![results image](https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/X-%3EEnglish.png)
+
+## More XLS-R models for `{lang}` -> `en` Speech Translation
+
+- [Wav2Vec2-XLS-R-300M-21-EN](https://huggingface.co/facebook/wav2vec2-xls-r-300m-21-to-en)
+- [Wav2Vec2-XLS-R-1B-21-EN](https://huggingface.co/facebook/wav2vec2-xls-r-1b-21-to-en)
+- [Wav2Vec2-XLS-R-2B-21-EN](https://huggingface.co/facebook/wav2vec2-xls-r-2b-21-to-en)
+- [Wav2Vec2-XLS-R-2B-22-16](https://huggingface.co/facebook/wav2vec2-xls-r-2b-22-to-16)